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REVIEW 4 major objections 5 minor 1 cited by

AerialGo: Walking-through City View Generation from Aerial Perspectives

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Ground-level city views from aerial photos alone

desk verdict Solid dataset and a sensible diffusion approach, but the real-city claim rests entirely on synthetic UE5 experiments and mixed metrics—worth reviewing, not accepting as is. read the letter →

arxiv 2412.00157 v1 pith:MXSPQ4FA submitted 2024-11-29 cs.CV cs.LG

Fuqiang Zhao , Yijing Guo , Siyuan Yang , Xi Chen , Luo Wang , Lan Xu , Yingliang Zhang , Yujiao Shi
show 1 more author
Jingyi Yu
This is my paper · ORCID
classification cs.CVcs.LG
keywords aerial-to-groundviewgenerationmulti-viewdiffusion3DurbanreconstructionGaussianSplattingnovelsynthesiscity-scaledatasetprivacy-preservingimaging
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

City-scale 3D reconstruction usually depends on collections of ground-level images, which are costly to gather and can expose license plates, faces, and other personal data. AerialGo proposes a multi-view diffusion model that generates realistic street-level (walking-through) views conditioned only on aerial imagery and a coarse point cloud derived from it. The paper claims these generated ground views, when added to standard reconstruction pipelines such as 3D Gaussian Splatting, significantly improve ground-level rendering quality. If true, detailed urban models for planning, navigation, and AR/VR could be built from aerial captures alone, sidestepping ground-level data collection and its privacy burden. The accompanying AerialGo dataset, 3.45 million paired aerial and ground images over 134 km² of synthetic cities, is released to support training of such generative models.

What carries the argument

The load-bearing mechanism is a multi-view latent diffusion model initialized from ImageDream and adapted to the aerial-to-ground setting. The denoising UNet processes a stack of $N=3$ encoded aerial reference latents concatenated with the noisy ground-view latent, using 3D self-attention (1D attention across views plus 2D attention within each image) so that multi-view aerial context is shared coherently. Structural conditioning comes from rendering the reconstructed 3D point cloud from the target ground camera, encoding that render with a CLIP encoder, and injecting the embeddings through cross-attention and the residual blocks, together with camera embeddings; classifier-free guidance is applied with a scale of 5.0. This design is what lets the model bridge the large angular gap between oblique aerial cameras and near-horizontal pedestrian viewpoints, and the same point-cloud conditioning is what lets the generated views stay anchored to real geometry when used as reconstruction priors.

What would settle it

Take a real urban area with both drone/satellite aerial imagery and a modest ground-truth set of street-level photos, run the AerialGo pipeline to generate ground views from the aerial data, and compare the generated views and the resulting 3DGS reconstruction against the real ground-truth images. A substantial drop in PSNR/SSIM/LPIPS relative to the synthetic results would show that the method's real-city promise is not met.

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Extended reading notes

Core claim

The paper's central claim is that a diffusion model can synthesize photorealistic ground-level views from aerial images, and that these synthetic views act as effective priors for 3D urban reconstruction. Trained on synthetic Unreal Engine 5 data from the new AerialGo dataset and from MatrixCity, the model takes three nearby aerial reference images and a point-cloud rendering of the target ground camera, and denoises a ground-view latent through a UNet with 3D self-attention, producing an image aligned with the requested viewpoint. When these generated images are fed into 3DGS, CityGaussian, or GigaGS along with a perceptual loss and a skybox model, the paper reports markedly better ground-level PSNR, SSIM, and LPIPS than aerial-only reconstruction, and better cross-view synthesis than prior diffusion-based novel-view methods.

Load-bearing premise

The load-bearing assumption is that a diffusion model trained only on Unreal Engine 5 synthetic cities will transfer to real aerial imagery of real cities, where the reconstructed point clouds are noisier and the visual appearance differs.

Editorial extensions

If this is right

  • City-scale 3D reconstruction can be driven by aerial imagery alone; ground-level priors are produced synthetically, removing the need for dedicated ground capture campaigns.
  • The privacy concerns associated with street-level imagery (faces, license plates) are bypassed because no real ground images are collected or stored.
  • Reconstruction methods built on 3D Gaussian Splatting, NeRF, or MVS can be upgraded with a plug-in perceptual loss and skybox to exploit the generated priors, improving ground-level realism without architectural changes to the core representation.
  • The AerialGo dataset, with its paired aerial/ground views, depth, segmentation, and weather/lighting controls, gives the research community a training resource for cross-view generation and city-scale generative modeling that previously did not exist at this scale.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The synthetic-only training means the strongest untested claim is transfer to real cities; a natural testbed is a real drone or satellite dataset with a small set of held-out street-level images for evaluation.
  • Because the conditioning signal is a point cloud rendered from the ground camera, the framework's real-world ceiling is set by the quality of aerial SfM/MVS; noisy or incomplete real point clouds would weaken the structural prior that the ablation shows is essential.
  • The same architecture could plausibly be extended from image generation to video (walking-through sequences) by replacing the 3D self-attention with temporal attention, which would move the method closer to street-level fly-through rendering.
  • The privacy argument extends beyond data collection: generated ground views contain no real personal data, so models trained on them could be shared or deployed in regulated settings without the anonymization overhead of real street imagery.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes AerialGo, a multi-view diffusion framework that generates ground-level city views from aerial images and uses them as priors for 3D reconstruction with 3DGS, CityGaussian, and GigaGS. It also introduces a large Unreal Engine 5 dataset of paired aerial and ground images covering 134 km² with camera, depth, and segmentation annotations. Experiments on the AerialGo and MatrixCity datasets compare reconstructions with and without generated priors, compare against existing novel-view-synthesis methods, and ablate the number of conditioning views and the point-render condition. The central claim is that generated ground views substantially improve the realism and structural coherence of ground-level urban reconstructions while avoiding direct ground-level data collection.

Significance. If fully supported, the result would be a useful step toward using generative priors to relieve the ground-data bottleneck in city-scale reconstruction, and the proposed 3.45M-image dataset would be a valuable community resource. The paper is also commendable for evaluating the priors on multiple reconstruction backbones and for including ablations of the conditioning design. There is no equation-level circularity: the diffusion model is trained on paired data and the reconstruction is evaluated against held-out ground truth. However, the evidence for the central claim is mixed: Table 2 shows performance degradation on AerialGo City A for two of three baselines, and all quantitative results are on synthetic UE5 renders, leaving the real-city applicability claimed in the introduction untested. The central method may be defensible, but the current manuscript needs additional experiments and more careful claims before it can be accepted.

major comments (4)
  1. [Section 5.1, Table 2] The central claim of 'significantly improved reconstructions' is not consistently supported by the reported numbers. On AerialGo City A, adding priors lowers SSIM for 3DGS (0.686 to 0.626) and CityGaussian (0.674 to 0.654) and worsens LPIPS for 3DGS (0.328 to 0.397) and CityGaussian (0.350 to 0.387); only GigaGS improves across all three metrics. Because the table averages metrics over aerial and ground views, the paper should report ground-view-only metrics and per-block breakdowns, together with confidence intervals or significance tests, before the claim in Section 5.1 can be evaluated.
  2. [Section 4.2 and Table 2] The 'with priors' condition changes three factors simultaneously: generated ground images are added, an LPIPS loss is introduced, and skybox modeling is added. The reported improvements therefore cannot be attributed to the generated ground-view priors alone. An ablation that isolates the generated priors (for example, generated images combined with the same LPIPS loss and skybox, versus no generated images under the same losses and skybox) is required to support the abstract's claim that AerialGo significantly enhances ground-level realism.
  3. [Sections 1, 4.1, and 5] All quantitative experiments are conducted on Unreal Engine 5 renders (AerialGo and MatrixCity), with perfect camera poses, clean depth, and synthetic appearance, yet the introduction and abstract frame the method as a solution for real cities where ground-level capture is unavailable. The point-cloud conditioning in Section 4.1 assumes that aerial point clouds reconstructed from images are accurate enough to render a useful CLIP condition; real SfM/MVS point clouds are typically noisier, sparser, and more incomplete. The paper needs at least one experiment on real oblique aerial data or an explicit sensitivity analysis to conditioning noise; without this, the real-city applicability claim is unsubstantiated.
  4. [Section 5.1, Table 3] The comparison with LucidDreamer, ViewCraft, and MotionCtrl mentions 'necessary adaptations' without specifying what was adapted, which hyperparameters were used, or how the models were trained or fine-tuned. The reported PSNR values (7.98–10.46) are far below typical novel-view-synthesis results, so it is difficult to rule out configuration artifacts. Please provide implementation details or release code so that the comparison is verifiable and reproducible.
minor comments (5)
  1. [Section 4.1 and References] The sentence 'such as ImageDream [44], LucidDreamer [7], ReconFusion [54], and ViewCraft [47]' appears to swap citation numbers: ViewCraft is reference [54] and ReconFusion is reference [47].
  2. [Section 5] The text cites 'CityGaussian [17]', but reference [17] is the City3D paper, not the CityGaussian paper; the citation should be corrected to the appropriate entry.
  3. [Table 3 and Section 5.1] There are minor naming inconsistencies: 'MotionCrtl' should be 'MotionCtrl', and the paper alternates between 'GigaGs' and 'GigaGS'.
  4. [Section 5.2, Table 4] The ablation table does not state which dataset it is run on or how the metrics are averaged over views, and the '0-view condition' row is not clearly distinguished from the 'w/o point render condition' row.
  5. [References] References [42] and [43] both refer to the same Mega-NeRF paper; the duplicate should be removed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: AerialGo's generation-to-reconstruction pipeline is evaluated on held-out city blocks and the external MatrixCity benchmark, with no load-bearing self-citation or definitional reduction.

full rationale

AerialGo's derivation chain is not circular. The diffusion model (Sec. 4.1) is trained to predict ground-view images from aerial latents, camera embeddings, and CLIP embeddings of point-cloud renders; the reconstruction experiments (Sec. 5.1) then compare 3DGS, CityGaussian, and GigaGS with and without these generated views against ground-truth views on held-out 10% city blocks, including the external MatrixCity dataset. The generated views are outputs of a learned conditional generator, not fitted parameters of the reconstruction objective, and the conditioning inputs (aerial images, point-cloud renders, camera parameters) do not include the evaluation ground-truth images. The choice of N=3 reference views is justified by an explicit ablation (Table 4) rather than assumed by construction. Self-citations in the reference list (e.g., [4], [8], [58], [59]) appear only as related work and are not load-bearing premises; no uniqueness theorem or prior-work ansatz is invoked to force the architecture. The paper's exclusive reliance on Unreal Engine 5 renders raises a legitimate external-validity question about synthetic-to-real transfer, but that is a missing empirical test, not a circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on three classes of assumptions: (1) the UE5 synthetic environments adequately represent real urban scenes, which is untested; (2) pretrained components (ImageDream, CLIP, autoencoder) transfer to the urban domain; (3) aerial-derived point clouds are accurate enough to guide ground-view synthesis. The free parameters are training and architecture choices selected by hand or by ablation, not constants derived from first principles. No new physical or metaphysical entities are introduced; the AerialGo dataset is a resource, not an invented entity.

free parameters (4)
  • Number of reference aerial views N = 3
    Ablated in Table 4; 3 chosen as best. This is a hyperparameter selected on the validation split, not a physical constant.
  • Classifier-free guidance scale = 5.0
    Set in Section 4.3; standard CFG magnitude, chosen by hand.
  • Noise scale increase schedule at DDIM sampling
    Section 4.1 states the noise scale is increased at each timestep without quantifying the schedule; any claim of improved generalization depends on this unspecified choice.
  • Skybox parameters = 100k primitives, 10x scene diameter
    Section 4.2: chosen by hand to model sky; affects reconstruction quality but not the core generative claim.
assumptions (4)
  • domain assumption Synthetic UE5 data is representative of real urban scenes.
    The method is trained and evaluated only on AerialGo and MatrixCity, both Unreal Engine 5 renders; the paper's stated application is real cities (Section 1). No real-world aerial data is tested.
  • domain assumption Point clouds reconstructed from aerial images are accurate enough to condition ground-view generation.
    Section 4.1: the method renders the reconstructed point cloud from the ground camera and uses CLIP embeddings as conditioning; if aerial-only SfM/MVS fails on real city-scale data, the conditioning signal is corrupted.
  • domain assumption ImageDream's pretrained weights transfer to urban scenes.
    Section 4.1: the model is initialized from ImageDream, pre-trained on Objaverse object data; urban scenes differ significantly, and the paper only modifies noise scale to compensate.
  • domain assumption LPIPS loss and skybox improve reconstruction with generated priors.
    Section 4.2: these modifications are introduced to mitigate high-frequency inconsistencies; their effectiveness is not separately ablated.

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Cite this review

Pith. "Pith review of AerialGo: Walking-through City View Generation from Aerial Perspectives." pith.science (2026). https://pith.science/paper/MXSPQ4FA

@misc{pith2026241200157,
  author       = {Pith},
  title        = {Pith review of: AerialGo: Walking-through City View Generation from Aerial Perspectives},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MXSPQ4FA}},
  note         = {Machine review of arXiv:2412.00157}
}
read the original abstract

High-quality 3D urban reconstruction is essential for applications in urban planning, navigation, and AR/VR. However, capturing detailed ground-level data across cities is both labor-intensive and raises significant privacy concerns related to sensitive information, such as vehicle plates, faces, and other personal identifiers. To address these challenges, we propose AerialGo, a novel framework that generates realistic walking-through city views from aerial images, leveraging multi-view diffusion models to achieve scalable, photorealistic urban reconstructions without direct ground-level data collection. By conditioning ground-view synthesis on accessible aerial data, AerialGo bypasses the privacy risks inherent in ground-level imagery. To support the model training, we introduce AerialGo dataset, a large-scale dataset containing diverse aerial and ground-view images, paired with camera and depth information, designed to support generative urban reconstruction. Experiments show that AerialGo significantly enhances ground-level realism and structural coherence, providing a privacy-conscious, scalable solution for city-scale 3D modeling.

Figures

Figures reproduced from arXiv: 2412.00157 by the authors.

Figure 1
Figure 1. Overview of the AerialGo dataset and results. (a) The AerialGo dataset is a large-scale, multi-view dataset, encompassing aerial and ground perspectives, and multi-attribute dataset. (b) Leveraging the AerialGo dataset, we introduce the AerialGo method, an innovative multi-view diffusion framework designed to synthesize photorealistic ground-level imagery from aerial observations, enabling enhanced urban scene recon… view at source ↗
Figure 2
Figure 2. Overview of the dataset and data collection process. This figure showcases an example of our urban city model, highlighting the block partitioning, the design of aerial and ground trajectories, as well as the dynamic rendering capabilities. 2. Related Work 3D Reconstruction for Large-scale Scenes. City-scale 3D reconstruction has a long history in photogrammetry and computer vision, originating from early techniques… view at source ↗
Figure 3
Figure 3. Pipeline of the AerialGo method. Starting with a target ground view, we first select reference images from the nearest aerial views and encode them using a pretrained auto-encoder. The diffusion model then processes the encoded aerial features along with random noise at the ground view, passing the adapted features through 3D self-attention layers. Additionally, CLIP embeddings of the ground-view point cloud render … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of 3D reconstruction methods with or without our generated ground view priors. * notes that the method is implemented by ourselves. Method Settings AerialGo City A AerialGo City B MatrixCity Dataset PSNR↑ SSIM↑ LPIPS↓ PSNR↑ SSIM↑ LPIPS↓ PSNR↑ SSI…
Figure 5
Figure 5. Figure 5: Qualitative comparison of generative NVS methods on AerialGo and MatrixCity Dataset. Compared with MotionCtrl [45], LucidDreamer [7], and ViewCraft [54], our results aligns well with the target image [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Qualitative ablation on different numbers of condi￾tion views. We found that conditioning on three aerial views pro￾vides the best result. tic aerial-level perspectives but struggle to render accurate ground-level views. In contrast, by incorporating ground￾view images…

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination

    cs.CV 2025-12 conditional novelty 7.0 of 10

    SkyLume contributes 10 real-world UAV urban regions captured at morning, noon, and afternoon with LiDAR-based ground truth, plus the Temporal Consistency Coefficient metric for cross-time albedo stability.

Reference graph

Works this paper leans on

61 extracted references · 31 canonical work pages · cited by 1 Pith paper

  1. [1]

    Building rome in a day

    Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Si- mon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM , 54 (10):105–112, 2011. 2, 3

    work page 2011

  2. [2]

    Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 5855–5864,

  3. [3]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3

    arXiv 2023

  4. [4]

    Mvsnerf: Fast general- izable radiance field reconstruction from multi-view stereo

    Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast general- izable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124–14133, 2021. 3

    work page 2021

  5. [5]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. InEuropean con- ference on computer vision, pages 333–350. Springer, 2022. 3

    2022

  6. [6]

    Gigags: Scaling up planar-based 3d gaus- sians for large scene surface reconstruction

    Junyi Chen, Weicai Ye, Yifan Wang, Danpeng Chen, Di Huang, Wanli Ouyang, Guofeng Zhang, Yu Qiao, and Tong He. Gigags: Scaling up planar-based 3d gaus- sians for large scene surface reconstruction. arXiv preprint arXiv:2409.06685, 2024. 7

    arXiv 2024

  7. [7]

    Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes

    Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 2, 5, 8

    arXiv 2023

  8. [8]

    Letsgo: Large-scale garage modeling and ren- dering via lidar-assisted gaussian primitives

    Jiadi Cui, Junming Cao, Yuhui Zhong, Liao Wang, Fuqiang Zhao, Penghao Wang, Yifan Chen, Zhipeng He, Lan Xu, Yu- jiao Shi, et al. Letsgo: Large-scale garage modeling and ren- dering via lidar-assisted gaussian primitives. arXiv preprint arXiv:2404.09748, 2024. 3

    arXiv 2024

Show all 61 references
  1. [9]

    Vision-based uav self- positioning in low-altitude urban environments.IEEE Trans- actions on Image Processing, 33:493–508, 2024

    Ming Dai, Enhui Zheng, Zhenhua Feng, Lei Qi, Jiedong Zhuang, and Wankou Yang. Vision-based uav self- positioning in low-altitude urban environments.IEEE Trans- actions on Image Processing, 33:493–508, 2024. 4

    2024

  2. [10]

    Objaverse: A universe of annotated 3d objects

    Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec...

    2023

  3. [11]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501–5510, 2022. 3

    2022

  4. [12]

    Cat3d: Create anything in 3d with multi-view diffusion models

    Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T Barron, and Ben Poole. Cat3d: Create anything in 3d with multi-view diffusion models. arXiv preprint arXiv:2405.10314, 2024. 2

    2024 arXiv

  5. [13]

    Multi- view stereo revisited

    Michael Goesele, Brian Curless, and Steven M Seitz. Multi- view stereo revisited. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), pages 2402–2409. IEEE, 2006. 3

    2006

  6. [14]

    Imagen video: High definition video generation with diffusion mod- els

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion mod- els. arXiv preprint arXiv:2210.02303, 2022. 3

    arXiv

  7. [15]

    Video dif- fusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video dif- fusion models. Advances in Neural Information Processing Systems, 35:8633–8646, 2022. 3

    2022

  8. [16]

    2d gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically ac- curate radiance fields. SIGGRAPH, 2024. 3

    2024

  9. [17]

    City3d: Large-scale building reconstruction from airborne lidar point clouds

    Jin Huang, Jantien Stoter, Ravi Peters, and Liangliang Nan. City3d: Large-scale building reconstruction from airborne lidar point clouds. Remote Sensing, 14(9):2254, 2022. 7

    2022

  10. [18]

    Video interpolation with diffu- sion models

    Siddhant Jain, Daniel Watson, Eric Tabellion, Ben Poole, Janne Kontkanen, et al. Video interpolation with diffu- sion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 7341– 7351, 2024. 3

    2024

  11. [19]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  12. [20]

    A hierarchical 3d gaussian representation for real-time ren- dering of very large datasets

    Bernhard Kerbl, Andreas Meuleman, Georgios Kopanas, Michael Wimmer, Alexandre Lanvin, and George Drettakis. A hierarchical 3d gaussian representation for real-time ren- dering of very large datasets. ACM Transactions on Graph- ics, 43(4), 2024. 3

    2024

  13. [21]

    Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond

    Yixuan Li, Lihan Jiang, Linning Xu, Yuanbo Xiangli, Zhen- zhi Wang, Dahua Lin, and Bo Dai. Matrixcity: A large-scale city dataset for city-scale neural rendering and beyond. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3205–3215, 2023. 4, 6, 7

    2023

  14. [22]

    Sat2vid: Street-view panoramic video synthesis from a single satellite image

    Zuoyue Li, Zhenqiang Li, Zhaopeng Cui, Rongjun Qin, Marc Pollefeys, and Martin R Oswald. Sat2vid: Street-view panoramic video synthesis from a single satellite image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12436–12445, 2021. 3

    2021

  15. [23]

    Sat2scene: 3d urban scene genera- tion from satellite images with diffusion

    Zuoyue Li, Zhenqiang Li, Zhaopeng Cui, Marc Pollefeys, and Martin R Oswald. Sat2scene: 3d urban scene genera- tion from satellite images with diffusion. In Proceedings of 9 the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7141–7150, 2024. 3

    2024

  16. [24]

    InfiniCity: Infinite-scale city synthesis

    Chieh Hubert Lin, Hsin-Ying Lee, Willi Menapace, Menglei Chai, Aliaksandr Siarohin, Ming-Hsuan Yang, and Sergey Tulyakov. InfiniCity: Infinite-scale city synthesis. In Pro- ceedings of the IEEE/CVF international conference on com- puter vision, 2023. 3

    2023

  17. [25]

    Vastgaussian: Vast 3d gaus- sians for large scene reconstruction

    Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, You- liang Yan, and Wenming Yang. Vastgaussian: Vast 3d gaus- sians for large scene reconstruction. In CVPR, 2024. 2

    2024

  18. [26]

    Capturing, reconstructing, and simulating: the urbanscene3d dataset

    Liqiang Lin, Yilin Liu, Yue Hu, Xingguang Yan, Ke Xie, and Hui Huang. Capturing, reconstructing, and simulating: the urbanscene3d dataset. In ECCV, pages 93–109, 2022. 4

    2022

  19. [27]

    Lending orientation to neural networks for cross-view geo-localization

    Liu Liu and Hongdong Li. Lending orientation to neural networks for cross-view geo-localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5624–5633, 2019. 4

    2019

  20. [28]

    Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age

    Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age. arXiv preprint arXiv:2309.03453, 2023. 3

    2023 arXiv

  21. [29]

    Citygaussian: Real-time high-quality large-scale scene rendering with gaussians

    Yang Liu, He Guan, Chuanchen Luo, Lue Fan, Junran Peng, and Zhaoxiang Zhang. Citygaussian: Real-time high-quality large-scale scene rendering with gaussians. arXiv preprint arXiv:2404.01133, 2024. 2

    2024 arXiv

  22. [30]

    Geometry-aware satellite- to-ground image synthesis for urban areas

    Xiaohu Lu, Zuoyue Li, Zhaopeng Cui, Martin R Oswald, Marc Pollefeys, and Rongjun Qin. Geometry-aware satellite- to-ground image synthesis for urban areas. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 859–867, 2020. 3

    2020

  23. [31]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM, 65(1):99–106, 2021. 2, 3

    2021

  24. [32]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4):1–15, 2022. 3

    2022

  25. [33]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Milden- hall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3

    2022 arXiv

  26. [34]

    Sat2density: Faithful density learning from satellite-ground image pairs

    Ming Qian, Jincheng Xiong, Gui-Song Xia, and Nan Xue. Sat2density: Faithful density learning from satellite-ground image pairs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3683–3692, 2023. 3

    2023

  27. [35]

    Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps

    Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. InInternational Conference on Com- puter Vision (ICCV), 2021. 3

    2021

  28. [36]

    Structure- from-motion revisited

    Johannes L Sch ¨onberger and Jan-Michael Frahm. Structure- from-motion revisited. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 4104–4113, 2016. 3

    2016

  29. [37]

    Zero123++: a single image to consistent multi-view dif- fusion base model

    Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view dif- fusion base model. arXiv preprint arXiv:2310.15110, 2023. 3

    arXiv

  30. [38]

    Geometry-guided street-view panorama synthesis from satellite imagery

    Yujiao Shi, Dylan Campbell, Xin Yu, and Hongdong Li. Geometry-guided street-view panorama synthesis from satellite imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):10009–10022, 2022. 3

    2022

  31. [39]

    Mvdream: Multi-view diffusion for 3d gen- eration

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d gen- eration. arXiv preprint arXiv:2308.16512, 2023. 3

    2023 arXiv

  32. [40]

    Block-nerf: Scalable large scene neural view synthesis

    Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Prad- han, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Reco...

    2022

  33. [41]

    Coming down to earth: Satellite-to-street view synthesis for geo-localization

    Aysim Toker, Qunjie Zhou, Maxim Maximov, and Laura Leal-Taix´e. Coming down to earth: Satellite-to-street view synthesis for geo-localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6488–6497, 2021. 3

    2021

  34. [42]

    Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs

    Haithem Turki, Deva Ramanan, and Mahadev Satya- narayanan. Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12922–12931, 2022. 4

    2022

  35. [43]

    Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs

    Haithem Turki, Deva Ramanan, and Mahadev Satya- narayanan. Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12922–12931, 2022. 2

    2022

  36. [44]

    Imagedream: Image-prompt multi-view diffusion for 3d generation

    Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv preprint arXiv:2312.02201, 2023. 3, 5, 6

    2023 arXiv

  37. [45]

    Motionctrl: A unified and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Pa- pers, pages 1–11, 2024. 3, 8

    2024

  38. [46]

    Uavd4l: A large-scale dataset for uav 6-dof localization

    Rouwan Wu, Xiaoya Cheng, Juelin Zhu, Xuxiang Liu, Mao- jun Zhang, and Shen Yan. Uavd4l: A large-scale dataset for uav 6-dof localization. In International Conference on 3D Vision (3DV), 2024. 4

    2024

  39. [47]

    Reconfusion: 3d reconstruction with diffusion priors

    Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P Srinivasan, Dor Verbin, Jonathan T Barron, Ben Poole, et al. Reconfusion: 3d reconstruction with diffusion priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patt...

    2024

  40. [48]

    Cross-view panorama image synthesis

    Songsong Wu, Hao Tang, Xiao-Yuan Jing, Haifeng Zhao, Jianjun Qian, Nicu Sebe, and Yan Yan. Cross-view panorama image synthesis. IEEE Transactions on Multimedia , 25: 3546–3559, 2022. 3

    2022

  41. [49]

    Geospecific view generation– geometry-context aware high-resolution ground view infer- ence from satellite views

    Ningli Xu and Rongjun Qin. Geospecific view generation– geometry-context aware high-resolution ground view infer- ence from satellite views. arXiv preprint arXiv:2407.08061,

    arXiv

  42. [50]

    Uav-visloc: A large- scale dataset for uav visual localization

    Wenjia Xu, Yaxuan Yao, Jiaqi Cao, Zhiwei Wei, Chunbo Liu, Jiuniu Wang, and Mugen Peng. Uav-visloc: A large- scale dataset for uav visual localization. arXiv preprint arXiv:2405.11936, 2024. 4

    2024 arXiv

  43. [51]

    Crossloc: Scalable aerial localiza- tion assisted by multimodal synthetic data

    Qi Yan, Jianhao Zheng, Simon Reding, Shanci Li, and Iordan Doytchinov. Crossloc: Scalable aerial localiza- tion assisted by multimodal synthetic data. arXiv preprint arXiv:2112.09081, 2021. 4

    2021 arXiv

  44. [52]

    Street gaussians for modeling dynamic ur- ban scenes, 2024

    Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians for modeling dynamic ur- ban scenes, 2024. 3

    2024

  45. [53]

    Urbanbis: a large-scale benchmark for fine- grained urban building instance segmentation

    Guoqing Yang, Fuyou Xue, Qi Zhang, Ke Xie, Chi-Wing Fu, and Hui Huang. Urbanbis: a large-scale benchmark for fine- grained urban building instance segmentation. In ACM SIG- GRAPH 2023 Conference Proceedings , pages 1–11, 2023. 4

    2023

  46. [54]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024. 2, 3, 5, 8

    2024 arXiv

  47. [55]

    Mip-splatting: Alias-free 3d gaussian splat- ting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 19447–19456,

  48. [56]

    Predicting ground-level scene layout from aerial imagery

    Menghua Zhai, Zachary Bessinger, Scott Workman, and Nathan Jacobs. Predicting ground-level scene layout from aerial imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 867–875,

  49. [57]

    Drone-assisted road gaussian splatting with cross- view uncertainty

    Saining Zhang, Baijun Ye, Xiaoxue Chen, Yuantao Chen, Zongzheng Zhang, Cheng Peng, Yongliang Shi, and Hao Zhao. Drone-assisted road gaussian splatting with cross- view uncertainty. arXiv preprint arXiv:2408.15242 , 2024. 4

    2024 arXiv

  50. [58]

    Human performance modeling and ren- dering via neural animated mesh

    Fuqiang Zhao, Yuheng Jiang, Kaixin Yao, Jiakai Zhang, Liao Wang, Haizhao Dai, Yuhui Zhong, Yingliang Zhang, Minye Wu, Lan Xu, et al. Human performance modeling and ren- dering via neural animated mesh. ACM Transactions on Graphics (TOG), 41(6):1–17, 2022. 3

    2022

  51. [59]

    Humannerf: Efficiently gen- erated human radiance field from sparse inputs

    Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, and Lan Xu. Humannerf: Efficiently gen- erated human radiance field from sparse inputs. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7743–7753, 2022

    2022

  52. [60]

    Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes

    Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages...

    2024

  53. [61]

    Vigor: Cross- view image geo-localization beyond one-to-one retrieval

    Sijie Zhu, Taojiannan Yang, and Chen Chen. Vigor: Cross- view image geo-localization beyond one-to-one retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 3640–3649, 2021. 4 11

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.